Assessing Performance of a Genetic Test for Determining the Breed Composition of Mixed Breed Dogs Using F1 Hybrids
A.L. Watson; P.G. Jones; A.J. Martin; R.J. Stark; P.J. Markwell
Mixed breed dogs comprise approximately 50% of the dog population in the USA. A single nucleotide polymorphism (SNP) based test designed to determine the breed composition of mixed breed dogs has recently been made commercially availablea. The aim of this study was to assess the performance of this test in known mixed breed dogs. First cross (F1) hybrids containing breeds from six of the seven American Kennel Club breed groups were included in the study.
Blood samples were collected under veterinary supervision from 85 F1 hybrids bred from parents registered to a recognised Kennel Club. DNA was extracted and typed at more than 300 different SNPs across the genome using selective hybridization and PCR amplification, followed by a discriminatory single base-pair primer extension reaction. The SNP variants were detected by mass spectrometry. A Bayesian generative model was then used to infer the family tree of a dog from comparison of detected genotypes with 134 breed signatures developed previously from more than 8000 pure bred dogs. Inference was performed on eleven different family tree models, and the best-fit model selected using the deviance information criterion.
Fifteen different breeds, the most numerous of which were Labrador Retriever, Golden Retriever and Poodle were represented amongst the 85 dogs. Sensitivity of breed detection, calculated as true positive calls (TP) / (true positive calls + false negative calls), was 95%. Positive predictive value (TP / TP + false positive calls) of breed detection was 84%.
These data indicate that this type of test can be used as a first step in understanding the genetics of mixed breed dogs and may have application in both clinical practice and research.